



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			'''▁'''

__UpperCAmelCase			  =			{'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__UpperCAmelCase			  =			{
    '''facebook/xglm-564M''': 2_048,
}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   List[str]  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   Optional[int]  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   List[Any]  	=    ["input_ids", "attention_mask"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="<unk>",     SCREAMING_SNAKE_CASE_="<pad>",     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          UpperCamelCase       :    Optional[Any]		      =      {} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          UpperCamelCase       :    Any		      =      7
          UpperCamelCase       :    Optional[int]		      =      [F"""<madeupword{i}>""" for i in range(self.num_madeup_words			)]

          UpperCamelCase       :    Dict		      =      kwargs.get('additional_special_tokens',     []			)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     sep_token=SCREAMING_SNAKE_CASE_,     cls_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     sp_model_kwargs=self.sp_model_kwargs,     **SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    List[str]		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(str(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Optional[Any]		      =      vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCamelCase       :    int		      =      1

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCamelCase       :    Dict		      =      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

          UpperCamelCase       :    Optional[int]		      =      len(self.sp_model			)
          UpperCamelCase       :    Any		      =      {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
          self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[str]		      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       def __getstate__(     self			)	->						List[Any]:
          UpperCamelCase       :    int		      =      self.__dict__.copy()
          UpperCamelCase       :    Union[str, Any]		      =      None
          UpperCamelCase       :    int		      =      self.sp_model.serialized_model_proto()
          return state
       def __setstate__(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    Any		      =      d

          # for backward compatibility
          if not hasattr(self,     'sp_model_kwargs'			):
             UpperCamelCase       :    Any		      =      {}

          UpperCamelCase       :    int		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:

          if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
          UpperCamelCase       :    Optional[int]		      =      [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False			)	->						List[int]:

          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_			)

          if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			))
          return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			))
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    str		      =      [self.sep_token_id]

          if token_ids_a is None:
             return len(sep + token_ids_a			) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]
       @property
       def    snake_case_			(     self			)	->						int:
          return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,     out_type=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
          UpperCamelCase       :    Union[str, Any]		      =      self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_			)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          UpperCamelCase       :    Dict		      =      ''.join(SCREAMING_SNAKE_CASE_			).replace(SCREAMING_SNAKE_CASE_,     ' '			).strip()
          return out_string






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
             return
          UpperCamelCase       :    Optional[int]		      =      os.path.join(
              SCREAMING_SNAKE_CASE_,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

          if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE_			) and os.path.isfile(self.vocab_file			):
             copyfile(self.vocab_file,     SCREAMING_SNAKE_CASE_			)
          elif not os.path.isfile(self.vocab_file			):
             with open(SCREAMING_SNAKE_CASE_,     'wb'			) as fi:
                UpperCamelCase       :    List[str]		      =      self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_			)

          return (out_vocab_file,)









'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



